class RecommendationTest2:
def setUp(self):
clean()
c = Category(name="C", external_id="1")
w = Weight(name="t", weight=1.0)
w.save()
c.save()
for i in range(0, 4):
User(name="U" + str(i), external_id=str(i),
token="t" + str(i)).save()
users = User.objects.all()
for i in range(0, 4):
Feature(name="F" + str(i)).save()
features = Feature.objects.all()
for i in range(0, 6):
Event(category=c,
external_id=str(i),
name='E' + str(i),
website='',
description='').save()
events = Event.objects.all()
Rating(event=events[1], user=users[0], rating=4).save()
Rating(event=events[4], user=users[0], rating=5).save()
Rating(event=events[1], user=users[1], rating=3).save()
Rating(event=events[3], user=users[1], rating=4).save()
Rating(event=events[5], user=users[2], rating=4).save()
Rating(event=events[0], user=users[3], rating=5).save()
Rating(event=events[2], user=users[3], rating=3).save()
EventFeature(event=events[0], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[1], feature=features[0], tf_idf=1,
weight=w).save()
EventFeature(event=events[1], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[2], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[2], feature=features[2], tf_idf=1,
weight=w).save()
EventFeature(event=events[3], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[4], feature=features[0], tf_idf=1,
weight=w).save()
EventFeature(event=events[4], feature=features[1], tf_idf=1,
weight=w).save()
EventFeature(event=events[4], feature=features[2], tf_idf=1,
weight=w).save()
EventFeature(event=events[5], feature=features[3], tf_idf=1,
weight=w).save()
from recommendation import Recommendation
self.recommendation = Recommendation()
def test_RFSP(self):
RR = [[-1, 4, -1, -1, 5, -1], [-1, 3, -1, 4, -1, -1],
[-1, -1, -1, -1, -1, 4], [5, -1, 3, -1, -1, -1]]
FF = [[-1, 1, -1, -1], [1, 1, -1, -1], [-1, 1, 1, -1], [-1, 1, -1, -1],
[1, 1, 1, -1], [-1, -1, -1, 1]]
SS = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
PP = [[2, 2, 1, 0], [1, 2, 0, 0], [0, 0, 0, 1], [0, 2, 1, 0]]
self.recommendation.init_basic_matrix()
if not compare_list(
RR, transform_matrix_list(self.recommendation.get_matrix_r())):
print 'error RR test_RFSP'
print RR, '\n', transform_matrix_list(
self.recommendation.get_matrix_r())
if not compare_list(
FF, transform_matrix_list(self.recommendation.get_matrix_f())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(
self.recommendation.get_matrix_f())
if not compare_list(
SS, transform_matrix_list(self.recommendation.get_matrix_s())):
print 'error SS test_RFSP'
print SS, '\n', transform_matrix_list(
self.recommendation.get_matrix_s())
if not compare_list(
PP, transform_matrix_list(self.recommendation.get_matrix_p())):
print 'error PP test_RFSP'
print PP, '\n', transform_matrix_list(
self.recommendation.get_matrix_p())
def test_FF_UF_IUF(self):
FF = [[2, 2, 1, 0], [1, 2, 0, 0], [0, 0, 0, 1], [0, 2, 1, 0]]
UF = [2, 3, 2, 1]
IUF = [
0.30102999566398114, 0.1249387366082999, 0.30102999566398114,
0.6020599913279623
]
self.recommendation.init_frequency_matrix()
if not compare_list(
FF, transform_matrix_list(
self.recommendation.get_matrix_ff())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(
self.recommendation.get_matrix_ff())
if not compare_list(
UF, transform_matrix_list(
self.recommendation.get_matrix_uf())):
print 'error UF test_RFSP'
print UF, '\n', transform_matrix_list(
self.recommendation.get_matrix_uf())
if not compare_list(
IUF, transform_matrix_list(
self.recommendation.get_matrix_iuf())):
print 'error IUF test_RFSP'
print IUF, '\n', transform_matrix_list(
self.recommendation.get_matrix_iuf())
def test_W_UU(self):
W = [[0.6020599913279623, 0.2498774732165998, 0.30102999566398114, -1],
[0.30102999566398114, 0.2498774732165998, -1, -1],
[-1, -1, -1, 0.6020599913279623],
[-1, 0.2498774732165998, 0.30102999566398114, -1]]
UU = [[-1, 0.95551065333456875, -1.0, 1.0],
[0.95551065333456875, -1, -1.0, 1], [-1.0, -1.0, -1, -1.0],
[1, 1, -1.0, -1]]
self.recommendation.compute_matrix_w()
self.recommendation.compute_matrix_uu()
if not compare_list(
W, transform_matrix_list(self.recommendation.get_matrix_w())):
print 'error W test_RFSP'
print W, '\n', transform_matrix_list(
self.recommendation.get_matrix_w())
if not compare_list(
UU, transform_matrix_list(
self.recommendation.get_matrix_uu())):
print 'error UU test_RFSP'
print UU, '\n', transform_matrix_list(
self.recommendation.get_matrix_uu())
def test_final(self):
events = Event.objects.all()
res = {events[0]: 3, events[2]: 5, events[4]: 6}
res = OrderedDict(sorted(res.items(), key=lambda t: t[1],
reverse=True))
if not compare_ordered_dict(
self.recommendation.compute_recommended_events(
User.objects.all()[1]), res):
print 'error final result'
print res
print self.recommendation.compute_recommended_events(
User.objects.all()[1])
class RecommendationTest2:
def setUp(self):
clean()
c = Category(name="C", external_id="1")
w = Weight(name="t", weight=1.0)
w.save()
c.save()
for i in range(0, 4):
User(name="U"+str(i), external_id=str(i), token="t"+str(i)).save()
users = User.objects.all()
for i in range(0, 4):
Feature(name="F"+str(i)).save()
features = Feature.objects.all()
for i in range(0, 6):
Event(category=c, external_id=str(i), name='E'+str(i), website='', description='').save()
events = Event.objects.all()
Rating(event=events[1], user=users[0], rating=4).save()
Rating(event=events[4], user=users[0], rating=5).save()
Rating(event=events[1], user=users[1], rating=3).save()
Rating(event=events[3], user=users[1], rating=4).save()
Rating(event=events[5], user=users[2], rating=4).save()
Rating(event=events[0], user=users[3], rating=5).save()
Rating(event=events[2], user=users[3], rating=3).save()
EventFeature(event=events[0], feature=features[1], tf_idf=1, weight=w).save()
EventFeature(event=events[1], feature=features[0], tf_idf=1, weight=w).save()
EventFeature(event=events[1], feature=features[1], tf_idf=1, weight=w).save()
EventFeature(event=events[2], feature=features[1], tf_idf=1, weight=w).save()
EventFeature(event=events[2], feature=features[2], tf_idf=1, weight=w).save()
EventFeature(event=events[3], feature=features[1], tf_idf=1, weight=w).save()
EventFeature(event=events[4], feature=features[0], tf_idf=1, weight=w).save()
EventFeature(event=events[4], feature=features[1], tf_idf=1, weight=w).save()
EventFeature(event=events[4], feature=features[2], tf_idf=1, weight=w).save()
EventFeature(event=events[5], feature=features[3], tf_idf=1, weight=w).save()
from recommendation import Recommendation
self.recommendation = Recommendation()
def test_RFSP(self):
RR = [[-1,4,-1,-1,5,-1],[-1,3,-1,4,-1,-1],[-1,-1,-1,-1,-1,4],[5,-1,3,-1,-1,-1]]
FF = [[-1,1,-1,-1],[1,1,-1,-1],[-1,1,1,-1],[-1,1,-1,-1],[1,1,1,-1],[-1,-1,-1,1]]
SS = [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]
PP = [[2,2,1,0],[1,2,0,0],[0,0,0,1],[0,2,1,0]]
self.recommendation.init_basic_matrix()
if not compare_list(RR, transform_matrix_list(self.recommendation.get_matrix_r())):
print 'error RR test_RFSP'
print RR, '\n', transform_matrix_list(self.recommendation.get_matrix_r())
if not compare_list(FF, transform_matrix_list(self.recommendation.get_matrix_f())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(self.recommendation.get_matrix_f())
if not compare_list(SS, transform_matrix_list(self.recommendation.get_matrix_s())):
print 'error SS test_RFSP'
print SS, '\n', transform_matrix_list(self.recommendation.get_matrix_s())
if not compare_list(PP, transform_matrix_list(self.recommendation.get_matrix_p())):
print 'error PP test_RFSP'
print PP, '\n', transform_matrix_list(self.recommendation.get_matrix_p())
def test_FF_UF_IUF(self):
FF = [[2,2,1,0],[1,2,0,0],[0,0,0,1],[0,2,1,0]]
UF = [2,3,2,1]
IUF = [0.30102999566398114, 0.1249387366082999, 0.30102999566398114, 0.6020599913279623]
self.recommendation.init_frequency_matrix()
if not compare_list(FF, transform_matrix_list(self.recommendation.get_matrix_ff())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(self.recommendation.get_matrix_ff())
if not compare_list(UF, transform_matrix_list(self.recommendation.get_matrix_uf())):
print 'error UF test_RFSP'
print UF, '\n', transform_matrix_list(self.recommendation.get_matrix_uf())
if not compare_list(IUF, transform_matrix_list(self.recommendation.get_matrix_iuf())):
print 'error IUF test_RFSP'
print IUF, '\n', transform_matrix_list(self.recommendation.get_matrix_iuf())
def test_W_UU(self):
W = [[0.6020599913279623, 0.2498774732165998, 0.30102999566398114, -1], [0.30102999566398114, 0.2498774732165998, -1, -1], [-1, -1, -1, 0.6020599913279623], [-1, 0.2498774732165998, 0.30102999566398114, -1]]
UU = [[-1, 0.95551065333456875, -1.0, 1.0], [0.95551065333456875, -1, -1.0, 1], [-1.0, -1.0, -1, -1.0], [1, 1, -1.0, -1]]
self.recommendation.compute_matrix_w()
self.recommendation.compute_matrix_uu()
if not compare_list(W, transform_matrix_list(self.recommendation.get_matrix_w())):
print 'error W test_RFSP'
print W, '\n', transform_matrix_list(self.recommendation.get_matrix_w())
if not compare_list(UU, transform_matrix_list(self.recommendation.get_matrix_uu())):
print 'error UU test_RFSP'
print UU, '\n', transform_matrix_list(self.recommendation.get_matrix_uu())
def test_final(self):
events = Event.objects.all()
res = {events[0]: 3, events[2]: 5, events[4] : 6}
res = OrderedDict(sorted(res.items(), key=lambda t: t[1], reverse=True))
if not compare_ordered_dict(self.recommendation.compute_recommended_events(User.objects.all()[1]), res):
print 'error final result'
print res
print self.recommendation.compute_recommended_events(User.objects.all()[1])
class RecommendationTest1:
def setUp(self):
clean()
c = Category(name="C", external_id="1")
w = Weight(name="t", weight=1.0)
w.save()
c.save()
for i in range(0, 3):
User(name="U" + str(i), external_id=str(i),
token="t" + str(i)).save()
users = User.objects.all()
for i in range(0, 5):
Feature(name="F" + str(i)).save()
features = Feature.objects.all()
for i in range(0, 5):
Event(category=c,
external_id=str(i),
name='E' + str(i),
website='',
description='').save()
events = Event.objects.all()
Rating(event=events[0], user=users[0], rating=1).save()
Rating(event=events[1], user=users[0], rating=2).save()
Rating(event=events[2], user=users[0], rating=4).save()
Rating(event=events[3], user=users[0], rating=5).save()
Rating(event=events[4], user=users[0], rating=3).save()
Rating(event=events[0], user=users[1], rating=3).save()
Rating(event=events[1], user=users[1], rating=2).save()
Rating(event=events[2], user=users[1], rating=5).save()
Rating(event=events[0], user=users[2], rating=1).save()
Rating(event=events[1], user=users[2], rating=4).save()
Rating(event=events[4], user=users[2], rating=3).save()
EventFeature(event=events[0],
feature=features[0],
tf_idf=0.2,
weight=w).save()
EventFeature(event=events[0],
feature=features[2],
tf_idf=0.8,
weight=w).save()
EventFeature(event=events[1],
feature=features[1],
tf_idf=0.8,
weight=w).save()
EventFeature(event=events[1],
feature=features[2],
tf_idf=1.0,
weight=w).save()
EventFeature(event=events[1],
feature=features[4],
tf_idf=0.3,
weight=w).save()
EventFeature(event=events[2],
feature=features[0],
tf_idf=1.0,
weight=w).save()
EventFeature(event=events[2],
feature=features[2],
tf_idf=0.3,
weight=w).save()
EventFeature(event=events[3],
feature=features[1],
tf_idf=0.2,
weight=w).save()
EventFeature(event=events[3],
feature=features[3],
tf_idf=0.4,
weight=w).save()
EventFeature(event=events[4],
feature=features[3],
tf_idf=0.8,
weight=w).save()
FeatureUser(user=users[0], feature=features[1], weight=0.4).save()
FeatureUser(user=users[0], feature=features[3], weight=0.8).save()
FeatureUser(user=users[0], feature=features[4], weight=0.3).save()
FeatureUser(user=users[1], feature=features[0], weight=1.0).save()
FeatureUser(user=users[1], feature=features[2], weight=0.5).save()
FeatureUser(user=users[2], feature=features[1], weight=0.2).save()
FeatureUser(user=users[2], feature=features[4], weight=0.6).save()
from recommendation import Recommendation
self.recommendation = Recommendation()
def test_RFSP(self):
RR = [[1, 2, 4, 5, 3], [3, 2, 5, -1, -1], [1, 4, -1, -1, 3]]
FF = [[0.2, -1, 0.8, -1, -1], [-1, 0.8, 1, -1, 0.3],
[1, -1, 0.3, -1, -1], [-1, 0.2, -1, 0.4, -1],
[-1, -1, -1, 0.8, -1]]
SS = [[0, 0.4, 0, 0.8, 0.3], [1, 0, 0.5, 0, 0], [0, 0.2, 0, 0, 0.6]]
PP = [[1, 0.2, 0.3, 1.2, 0], [1.2, 0, 1.1, 0, 0],
[0, 0.8, 1, 0.8, 0.3]]
self.recommendation.init_basic_matrix()
if not compare_list(
RR, transform_matrix_list(self.recommendation.get_matrix_r())):
print 'error RR test_RFSP'
print RR, '\n', transform_matrix_list(
self.recommendation.get_matrix_r())
if not compare_list(
FF, transform_matrix_list(self.recommendation.get_matrix_f())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(
self.recommendation.get_matrix_f())
if not compare_list(
SS, transform_matrix_list(self.recommendation.get_matrix_s())):
print 'error SS test_RFSP'
print SS, '\n', transform_matrix_list(
self.recommendation.get_matrix_s())
if not compare_list(
PP, transform_matrix_list(self.recommendation.get_matrix_p())):
print 'error PP test_RFSP'
print PP, '\n', transform_matrix_list(
self.recommendation.get_matrix_p())
def test_FF_UF_IUF(self):
FF = [[1, 0.6, 0.3, 2.0, 0.3], [2.2, 0, 1.6, 0, 0],
[0, 1.0, 1, 0.8, 0.9]]
UF = [2, 2, 3, 2, 2]
IUF = [
0.176091259056, 0.176091259056, 0.0, 0.176091259056, 0.176091259056
]
self.recommendation.init_frequency_matrix()
if not compare_list(
FF, transform_matrix_list(
self.recommendation.get_matrix_ff())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(
self.recommendation.get_matrix_ff())
if not compare_list(
UF, transform_matrix_list(
self.recommendation.get_matrix_uf())):
print 'error UF test_RFSP'
print UF, '\n', transform_matrix_list(
self.recommendation.get_matrix_uf())
if not compare_list(
IUF, transform_matrix_list(
self.recommendation.get_matrix_iuf())):
print 'error IUF test_RFSP'
print IUF, '\n', transform_matrix_list(
self.recommendation.get_matrix_iuf())
def test_W_UU(self):
W = [[
0.176091259056, 0.105654755433, -1, 0.352182518111, 0.0528273777167
], [0.387400769922, -1, -1, -1, -1],
[-1, 0.176091259056, -1, 0.140873007245, 0.15848213315]]
UU = [[-1, 1.0, 0.822730338093], [1.0, -1, -1],
[0.822730338093, -1, -1]]
self.recommendation.compute_matrix_w()
self.recommendation.compute_matrix_uu()
if not compare_list(
W, transform_matrix_list(self.recommendation.get_matrix_w())):
print 'error W test_RFSP'
print W, '\n', transform_matrix_list(
self.recommendation.get_matrix_w())
if not compare_list(
UU, transform_matrix_list(
self.recommendation.get_matrix_uu())):
print 'error UU test_RFSP'
print UU, '\n', transform_matrix_list(
self.recommendation.get_matrix_uu())
def test_final(self):
events = Event.objects.all()
res = {events[3]: 3, events[4]: 2}
res = OrderedDict(sorted(res.items(), key=lambda t: t[1],
reverse=True))
if not compare_ordered_dict(
self.recommendation.compute_recommended_events(
User.objects.all()[1]), res):
print 'error final result'
print res
print self.recommendation.compute_recommended_events(
User.objects.all()[1])
class RecommendationTest1:
def setUp(self):
clean()
c = Category(name="C", external_id="1")
w = Weight(name="t", weight=1.0)
w.save()
c.save()
for i in range(0, 3):
User(name="U"+str(i), external_id=str(i), token="t"+str(i)).save()
users = User.objects.all()
for i in range(0, 5):
Feature(name="F"+str(i)).save()
features = Feature.objects.all()
for i in range(0, 5):
Event(category=c, external_id=str(i), name='E'+str(i), website='', description='').save()
events = Event.objects.all()
Rating(event=events[0], user=users[0], rating=1).save()
Rating(event=events[1], user=users[0], rating=2).save()
Rating(event=events[2], user=users[0], rating=4).save()
Rating(event=events[3], user=users[0], rating=5).save()
Rating(event=events[4], user=users[0], rating=3).save()
Rating(event=events[0], user=users[1], rating=3).save()
Rating(event=events[1], user=users[1], rating=2).save()
Rating(event=events[2], user=users[1], rating=5).save()
Rating(event=events[0], user=users[2], rating=1).save()
Rating(event=events[1], user=users[2], rating=4).save()
Rating(event=events[4], user=users[2], rating=3).save()
EventFeature(event=events[0], feature=features[0], tf_idf=0.2, weight=w).save()
EventFeature(event=events[0], feature=features[2], tf_idf=0.8, weight=w).save()
EventFeature(event=events[1], feature=features[1], tf_idf=0.8, weight=w).save()
EventFeature(event=events[1], feature=features[2], tf_idf=1.0, weight=w).save()
EventFeature(event=events[1], feature=features[4], tf_idf=0.3, weight=w).save()
EventFeature(event=events[2], feature=features[0], tf_idf=1.0, weight=w).save()
EventFeature(event=events[2], feature=features[2], tf_idf=0.3, weight=w).save()
EventFeature(event=events[3], feature=features[1], tf_idf=0.2, weight=w).save()
EventFeature(event=events[3], feature=features[3], tf_idf=0.4, weight=w).save()
EventFeature(event=events[4], feature=features[3], tf_idf=0.8, weight=w).save()
FeatureUser(user=users[0], feature=features[1], weight=0.4).save()
FeatureUser(user=users[0], feature=features[3], weight=0.8).save()
FeatureUser(user=users[0], feature=features[4], weight=0.3).save()
FeatureUser(user=users[1], feature=features[0], weight=1.0).save()
FeatureUser(user=users[1], feature=features[2], weight=0.5).save()
FeatureUser(user=users[2], feature=features[1], weight=0.2).save()
FeatureUser(user=users[2], feature=features[4], weight=0.6).save()
from recommendation import Recommendation
self.recommendation = Recommendation()
def test_RFSP(self):
RR = [[1, 2, 4, 5, 3],[3, 2, 5, -1, -1],[1, 4, -1, -1, 3]]
FF = [[0.2, -1, 0.8, -1, -1],[-1, 0.8, 1, -1, 0.3],[1, -1, 0.3, -1, -1],[-1, 0.2, -1, 0.4, -1],[-1, -1, -1, 0.8, -1]]
SS = [[0, 0.4, 0, 0.8, 0.3],[1, 0, 0.5, 0, 0],[0, 0.2, 0, 0, 0.6]]
PP = [[1, 0.2, 0.3, 1.2, 0],[1.2, 0, 1.1, 0, 0],[0, 0.8, 1, 0.8, 0.3]]
self.recommendation.init_basic_matrix()
if not compare_list(RR, transform_matrix_list(self.recommendation.get_matrix_r())):
print 'error RR test_RFSP'
print RR, '\n', transform_matrix_list(self.recommendation.get_matrix_r())
if not compare_list(FF, transform_matrix_list(self.recommendation.get_matrix_f())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(self.recommendation.get_matrix_f())
if not compare_list(SS, transform_matrix_list(self.recommendation.get_matrix_s())):
print 'error SS test_RFSP'
print SS, '\n', transform_matrix_list(self.recommendation.get_matrix_s())
if not compare_list(PP, transform_matrix_list(self.recommendation.get_matrix_p())):
print 'error PP test_RFSP'
print PP, '\n', transform_matrix_list(self.recommendation.get_matrix_p())
def test_FF_UF_IUF(self):
FF = [[1, 0.6, 0.3, 2.0, 0.3],[2.2, 0, 1.6, 0, 0],[0, 1.0, 1, 0.8, 0.9]]
UF = [2, 2, 3, 2, 2]
IUF = [0.176091259056, 0.176091259056, 0.0, 0.176091259056, 0.176091259056]
self.recommendation.init_frequency_matrix()
if not compare_list(FF, transform_matrix_list(self.recommendation.get_matrix_ff())):
print 'error FF test_RFSP'
print FF, '\n', transform_matrix_list(self.recommendation.get_matrix_ff())
if not compare_list(UF, transform_matrix_list(self.recommendation.get_matrix_uf())):
print 'error UF test_RFSP'
print UF, '\n', transform_matrix_list(self.recommendation.get_matrix_uf())
if not compare_list(IUF, transform_matrix_list(self.recommendation.get_matrix_iuf())):
print 'error IUF test_RFSP'
print IUF, '\n', transform_matrix_list(self.recommendation.get_matrix_iuf())
def test_W_UU(self):
W = [[0.176091259056, 0.105654755433, -1, 0.352182518111, 0.0528273777167],[0.387400769922, -1, -1, -1, -1],[-1, 0.176091259056, -1, 0.140873007245, 0.15848213315]]
UU = [[-1, 1.0, 0.822730338093],[1.0, -1, -1],[0.822730338093, -1, -1]]
self.recommendation.compute_matrix_w()
self.recommendation.compute_matrix_uu()
if not compare_list(W, transform_matrix_list(self.recommendation.get_matrix_w())):
print 'error W test_RFSP'
print W, '\n', transform_matrix_list(self.recommendation.get_matrix_w())
if not compare_list(UU, transform_matrix_list(self.recommendation.get_matrix_uu())):
print 'error UU test_RFSP'
print UU, '\n', transform_matrix_list(self.recommendation.get_matrix_uu())
def test_final(self):
events = Event.objects.all()
res = {events[3]: 3, events[4]: 2}
res = OrderedDict(sorted(res.items(), key=lambda t: t[1], reverse=True))
if not compare_ordered_dict(self.recommendation.compute_recommended_events(User.objects.all()[1]), res):
print 'error final result'
print res
print self.recommendation.compute_recommended_events(User.objects.all()[1])